Investigating time distortion in Parkinson’s disease considering impaired frontoparietal network and changes in the brain dynamic

<i>Objective</i>. Parkinson's disease (PD) is a neurodegenerative disorder characterized by a range of motor and non-motor symptoms. Despite extensive research, the neural structure of time distortion, remains unclear. This study aimed to determine the neurobiological origins of time distortion by analyzing dynamic features in PD patients compared to control participants.<i>Approach</i>. We used PD and control electroencephalography (EEG) signals to investigate brain function during time distortion. The EEG signal was recorded during an interval-timing task. Following artifact reduction and EEG signal segmentation, dynamic features were extracted from each frequency band across all the channels. Channels showing significant discrepancies between the two groups were selected by statistical analysis. The features are sent to an Artificial Neural Network (ANN) classifier to evaluate their discriminative potential.<i>Main results.</i>The results indicated lower values of Lyapunov Exponent and Approximate Entropy along with higher value of Fractal dimension in PD which presented higher level of irregularity and randomness, particularly in the CPz, P5, P6, and C5 channels. The ANN classifier achieved 90% accuracy, 89% sensitivity, 87% F1 score, and 95% specificity in a 10-fold cross-validation.<i>Significance.</i>Significantly different channels were concentrated in the central and parietal areas of the brain and were linked to decision-making, maintenance, and retrieval of stored information, and working memory. Moreover, based on dynamic EEG analysis, it seems that disrupted connections between the basal ganglia and posterior parietal cortex in PD appear to compromise frontoparietal network dysfunction, which is associated with impaired temporal processing in patients with PD.